Microsoft has made available TypeScript 4.3, the latest release of its programming language that builds on JavaScript and follows the release candidate that was made available earlier this month.

New in this release is support for separate write types on properties, to address the issue of APIs converting values that are passed in before storing them if the developer has not explicitly defined what type a value is. As of 4.3, users can specify types for reading and writing to properties.

TypeScript 4.3 has also introduced the override keyword to use when extending classes. This is to avoid the unintended consequences if a developer does not make it explicit whether they meant to add a new method or override an existing one.

Breaking changes in this release stem from the removal of APIs that no browser implements in declarations generated for web contexts, while if a value with a union enum type is compared with a numeric literal that it could never be equal to, then the type-checker will now issue an error. See the Microsoft DevBlog for further details.

Red Hat provides Universal Base Images on Docker Hub
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Red Hat has rolled out Red Hat Universal Base Images on Docker Hub, claiming this will make it easier for organisations to build cloud-native applications based on tested and trusted components of Red Hat Enterprise Linux.

Red Hat Universal Base Images (UBI) are containerised operating system images that include complementary runtime languages and packages. They are Open Container Initiative (OCI)-compliant and freely redistributable, the firm said.

UBI images are offered in a variety of configurations tailored for different enterprise IT use cases. These include Standard, which provides the necessary runtimes and YUM repositories to build, deploy and share UBI-based containers, while Minimal contains the bare essentials for a lightweight RHEL-based image.

Previously, UBI could only be obtained from the Red Hat container catalogue. With availability extended to Docker Hub, it will be easier for UBI-based containers to be built and deployed anywhere, Red Hat claimed.

Databricks AutoML makes it easier to build machine learning models
DataBricks has announced a public preview of Databricks AutoML, a tool designed to help users build and deploy machine learning models by automating the heavy lifting of pre-processing, feature engineering and model training/tuning.

According to DataBricks, AutoML would allow users to select a dataset, then train and deploy a model entirely through a UI. It integrates with the Databricks ML ecosystem, including tracking trial run metrics and parameters with MLflow, allowing users to register and version control models in the Databricks Model Registry for deployment.

AutoML provides Python notebooks for every model, with the source code for each trial run so users can review, reproduce, and modify the code. Data scientists can use their expertise to add or modify cells to these generated notebooks, and use them to jumpstart ML development by bypassing the need to write boilerplate code.

GitHub champions OpenTelemetry
GitHub has announced it is working to adopt the CNCF OpenTelemetry project internally, as a way to improve observability of its own infrastructure. The GitHub team said the project has potential to transform how the entire industry approaches ways to observe and understand systems, and invited other developers to provide assistance.

OpenTelemetry introduces a common, vendor-neutral format for telemetry signals, called OpenTelemetry Protocol (OTLP). It also enables telemetry signals to be easily correlated with each other. GitHub said OpenTelemetry would enable it to build integrated and opinionated solutions for its engineers, so that designing for observability would be at the forefront of its engineer’s minds.

GitHub said that as its OpenTelemetry efforts progress, it intends to shift focus towards logging and metrics systems, and claimed it is contributing as much as possible back to the OpenTelemetry project for others to benefit from.

Digital.ai intros platform to improve Devops outcomes
Digital.ai has announced the Digital.ai Platform, which uses machine learning-based analytics in a bid to predict and prevent issues impacting software delivery and reliability.

The platform features end-to-end DevOps lifecycle orchestration capabilities, such as release management, risk management, and software delivery predictability. It has strong integration and collaboration with DevOps tools, including Azure DevOps, Chef, Jenkins, Atlassian Jira, ServiceNow and Selenium, according to Digital.ai.

CDK for Terraform 0.4 gets Go
HashiCorp has released CDK for Terraform 0.4, the latest update of the Cloud Development Kit for its Terraform infrastructure-as-code provisioning tool.

CDK for Terraform give developers the ability to write Terraform configurations in C#, Python, TypeScript, and Java, using all existing Terraform providers and Terraform modules. This release has added experimental Go support, one of the top requests from users, according to HashiCorp.

Also added is a new asset construct that makes it easy to reference files and folders that need to be deployed alongside newly provisioned resources such as applications, plus improved Terraform Cloud integration. The CDK for Terraform changelog contains a comprehensive list of enhancements and bug fixes applying to this release.

Databricks has partnered with Google Cloud to deliver its platform to businesses around the world.

Under the partnership, organisations can now use Databricks to create a lakehouse capable of data engineering, data science, machine learning, and analytics on Google Cloud’s global, scalable, secure and elastic network.

Databricks on Google Cloud will integrate with Google BigQuery’s open platform and will leverage Google Kubernetes Engine (GKE), therefore enabling customers to deploy Databricks in a fully containerised cloud environment for the first time.

According to Databricks, increasingly, Kubernetes and containers are the de facto orchestration system for enterprise workloads and applications running in the cloud.

Databricks on Google Cloud is built on GKE, Google Clouds secure, managed Kubernetes service, to support containerised deployments of Databricks in the cloud.

By adopting GKE as an operating environment, Databricks is able to leverage managed services for security, network policy, and compute and as a result, provide customers with increasing business value through Databricks analytics, AI, and ML capabilities.

Additionally, with GKE, Databricks increases its agility and the ability to accelerate the release of new features, quickly, at scale, and at lower cost.

Furthermore, Databricks states, with this integration businesses can extend their existing Databricks lakehouse capabilities, now running on Google Cloud, and can cross-leverage Google BigQuery for analytics, ultimately simplifying their data investments, increasing usage, and creating new, data-driven business models and opportunities.

According to the company, tight integration of Databricks with Google Clouds analytics solutions gives customers the ability to extend AI-driven insights across data lakes, data warehouses, and multiple business intelligence tools.

Furthermore, pre-built connectors integrate Databricks with BigQuery, Google Cloud Storage, Looker and Pub/Sub to unlock AI-driven insights, enable intelligent decision-making, and ultimately accelerate digital transformations through data-driven applications.

Under this new partnership, the two companies will continue to support the open source community, encourage open innovation and collaboration, making it easier for joint customers to build on open-source technologies.

Members of the joint ecosystem of partners have already committed to ensure integrations with Databricks on Google Cloud, including Accenture, Cognizant, Collibra, Confluent, Deloitte, Fishtown Analytics, Fivetran, Immuta, Informatica, Infoworks, Insight, MongoDB, Privacera, Qlik, SADA, SoftServe, Slalom, Tableau, TCS and Trifacta, among others.

Google Cloud CEO Thomas Kurian says, “Businesses with a strong foundation of data and analytics are well-positioned to grow and thrive in the next decade. We’re delighted to deliver Databricks lakehouse for AI and ML-driven analytics on Google Cloud.

“By combining Databricks capabilities in data engineering and analytics with Google Cloud’s global, secure network and our expertise in analytics and delivering containerised applications we can help companies transform their businesses through the power of data.”

Databricks CEO and cofounder Ali Ghodsi says, “This is a pivotal milestone that underscores our commitment to enable customer flexibility and choice with a seamless experience across cloud platforms.

“We are thrilled to partner with Google Cloud and deliver on our shared vision of a simplified, open, and unified data platform that supports all analytics and AI use-cases that will empower our customers to innovate even faster.”

Sunnyvale and San Francisco, Calif., February 17, 2021 – Today, Google Cloud and Databricks announced a new partnership to deliver Databricks at global scale on Google Cloud. Under the partnership, organizations can now use Databricks to create a lakehouse capable of data engineering, data science, machine learning, and analytics on Google Cloud’s global, scalable, and elastic network. Databricks on Google Cloud will deeply integrate with Google BigQuery’s open platform and will leverage Google Kubernetes Engine (GKE), enabling customers to deploy Databricks in a fully containerized cloud environment for the first time. With this integrated solution, organizations can unlock AI-driven insights, enable intelligent decision-making, and ultimately accelerate their digital transformations through data-driven applications.

“Businesses with a strong foundation of data and analytics are well-positioned to grow and thrive in the next decade,” said Thomas Kurian, CEO at Google Cloud. “We’re delighted to deliver Databricks’ lakehouse for AI and ML-driven analytics on Google Cloud. By combining Databricks’ capabilities in data engineering and analytics with Google Cloud’s global, secure network—and our expertise in analytics and delivering containerized applications—we can help companies transform their businesses through the power of data.”

“This is a pivotal milestone that underscores our commitment to enable customer flexibility and choice with a seamless experience across cloud platforms,” said Ali Ghodsi, CEO and co-founder of Databricks. “We are thrilled to partner with Google Cloud and deliver on our shared vision of a simplified, open, and unified data platform that supports all analytics and AI use-cases that will empower our customers to innovate even faster.”

“For Condé Nast, finding valuable insights from massive amounts of data is essential for creating world class content experiences that delight our global customers. Databricks on Google Cloud provides a lakehouse for our data engineers, data scientists and analysts to consolidate, collaborate, analyze, and use all of our global data to experiment and innovate quickly. We’re excited to see leaders like Google Cloud and Databricks come together to streamline and simplify getting value from data,” said Nana Essuman, Director of Data Engineering at Condé Nast.

Deploying Databricks rapidly and securely at global scale

Global businesses need the ability to quickly deploy applications at any scale, and the elasticity to scale them up or down depending on their needs. Delivering Databricks on Google Cloud enables customers to rapidly provision Databricks on Google Cloud’s global network, with advanced security and data protection controls required for highly-regulated industries, and with the flexibility to quickly adjust usage based on the needs of the business.

Additionally, customers will soon be able to deploy Databricks from the Google Cloud Marketplace, enabling simplified procurement and user provisioning, Single Sign-On, and unified billing.

Advancing analytics with Databricks, BigQuery, and Google Cloud AI Platform

Databricks on Google Cloud is tightly integrated with Google BigQuery, giving customers the freedom of choice and access to their choice of data analytics services. With this integration, businesses can extend their existing Databricks lakehouse capabilities, now running on Google Cloud, and can cross-leverage Google BigQuery for analytics, ultimately simplifying their data investments, increasing usage, and creating new, data-driven business models and opportunities.

Unique integrations between Databricks and Google Cloud include:

Tight integration of Databricks with Google Cloud’s analytics solutions, giving customers the ability to easily extend AI-driven insights across data lakes, data warehouses, and multiple business intelligence tools.
Pre-built connectors to seamlessly and quickly integrate Databricks with BigQuery, Google Cloud Storage, Looker and Pub/Sub.
Fast and scalable model training with AI Platform using the data workflows created in Databricks, and simplified deployment of models built in Databricks using AI Platform Prediction.
Delivering containerized Databricks deployments for the first time

Databricks on Google Cloud represents the first container-based deployments of Databricks, on any cloud.

Increasingly, Kubernetes and containers are the de facto orchestration system for enterprise workloads and applications running in the cloud. Databricks on Google Cloud is built on GKE, Google Cloud’s secure, managed Kubernetes service, to support containerized deployments of Databricks in the cloud. By adopting GKE as an operating environment, Databricks is able to leverage managed services for security, network policy, and compute and as a result, provide customers with increasing business value through Databricks analytics, AI, and ML capabilities. Additionally, with GKE, Databricks increases its agility and the ability to accelerate the release of new features, quickly, at scale, and at lower cost.

Supporting open source innovation and partner collaboration

Databricks and Google Cloud share a commitment to open innovation and open source software. Under this new partnership, the two companies will continue to support the open source community, encourage open innovation and collaboration, making it easier for joint customers to build on open-source technologies.

Additionally, members of our joint ecosystem of partners have committed to ensure seamless integrations with Databricks on Google Cloud, including Accenture, Cognizant, Collibra, Confluent, Deloitte, Fishtown Analytics, Fivetran, Immuta, Informatica, Infoworks, Insight, MongoDB, Privacera, Qlik, SADA, SoftServe, Slalom, Tableau, TCS and Trifacta among others.

On April 6, join Databricks CEO, Ali Ghodsi, and Google Cloud CEO, Thomas Kurian as they share more about the partnership and vision of an open, unified data analytics platform during a discussion hosted by TechCrunch; visit the event page for more information.

Google Cloud

Google Cloud provides organizations with leading infrastructure, platform capabilities and industry solutions. We deliver enterprise-grade cloud solutions that leverage Google’s cutting-edge technology to help companies operate more efficiently and adapt to changing needs, giving customers a foundation for the future. Customers in more than 150 countries turn to Google Cloud as their trusted partner to solve their most critical business problems.

Databricks

Databricks is the data and AI company. Thousands of organizations worldwide — including Comcast, Condé Nast, Nationwide, and H&M — rely on Databricks’ open and unified platform for data engineering, data science, machine learning, and analytics. Databricks is venture-backed and headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake, and MLflow, Databricks is on a mission to help data teams solve the world’s toughest problems. To learn more, follow Databricks on Twitter, LinkedIn, and Facebook.

The global business analytical report titled Cloud Computing market has recently been published by The Research Corporation Market to its extensive database. The global Cloud Computing market is examined on the basis of technological advancements and recent trends of the Cloud Computing sector. The market study has been evaluated on the basis of different aspects of the businesses such as drivers and restraints which will affect the progress of the companies. An informative data gathered from distinctive sources such as case studies from numerous industry experts, views and opinions of business leaders, among others further contribute to the authenticity of the report.

Across the globe, the Cloud Computing market has been fragmented into several regions such as North America, Latin America, Asia-Pacific, Europe, and Africa for studies of successful sales strategies implemented by top-level companies. Apart from this, the study offers business profiles of leading key players operating in market dominated regions such as Cloud Computing. The report also provides an effective analysis of investments and market shares for a better understanding of the market. The study includes an analysis of several segments along with its sub-segments. Moreover, it also offers curate data of the revenue generated in major regions such as Cloud Computing. It also helps to analyze the several key factors such as pricing structure and manufacturing base of different companies.

Top Key Players Included in This Report:

Databricks, Uptake Technologies Inc., Rubrik, D2iQ, Inc., Vlocity Inc., Confluent Inc., Stratoscale, and SecurityScorecard

Major highlights of this research report:

-In-depth analysis of the degree of competition across the globe.
-Estimation of Global Cloud Computing Market values and volumes.
– Global Cloud Computing Market analysis through industry analysis tools such as SWOT and Porter’s five analyses.
-Business profiling of prominent companies across the global regions like North America, Latin America, Middle East, Asia-Pacific, Africa, and Europe.
-Detailed elaboration on global market value, volume, and penetration.
– Global Cloud Computing Market growth projections.
-Detailed description on development policies and plans.
-Analytical study on drivers, restraints, opportunities, hindrances, inadequacies, challenges and strengths.

Competitive landscape of the global Cloud Computing market has been studied by elaborating significant players operating in the global regions. The finding of this statistical report not only provides guidelines for improving the growth of the companies but also provides effective indicators to help plan future market expansions. Furthermore, the study also discusses various strategies and standard operating procedures implemented by top-notch market companies.

The research study has taken the help of graphical presentation techniques such as info graphics, charts, tables, and pictures. It provides guidelines for both established players and new entrants in the Global Cloud Computing Market

Table of Contents:

Global Cloud Computing Market Overview
Economic Impact on Industry
Market Competition by Companies
Production, Revenue (Value) by Region
Market Analysis by Application
Cost Analysis
Industrial Chain, Sourcing Strategy and Downstream Buyers
Marketing Strategy Analysis, Distributors/Traders
Market Effect Factors Analysis
Global Cloud Computing Market Forecast

About Us:
Research Corporation Reports is a piece of the Absolute Markets Insights and offers premium dynamic measurable looking over, statistical surveying reports, examination and estimate information for businesses and governments around the world. Research Corporation Reports includes a thorough rundown of statistical surveying reports from many distributers around the world. We brag a database traversing basically every market class and a considerably progressively complete gathering of statistical surveying reports under these classifications and sub-classifications.

Databricks, the Data and AI Company, announced that Azure Databricks has received a Federal Risk and Authorization Management Program (FedRAMP) High Authority to Operate (ATO). This authorization validates Azure Databricks security and compliance for high-impact data analytics and AI across a wide range of public sector, industry, and enterprise use cases.

FedRAMP is a standardized approach to security assessment, authorization, and continuous monitoring for cloud services as defined by the National Institute of Standards and Technology (NIST). The ATO was granted by the FedRAMP Joint Authorization Board (JAB) which consists of representatives from the Department of Defense (DoD), Department of Homeland Security (DHS), and the General Services Administration (GSA).

At Veterans Affairs, we included Azure Databricks as part of our Microsoft Azure Authority to Operate (ATO),” said Joseph Fourcade, Lead Cyber Security Analyst, U.S. Department of Veterans Affairs Enterprise Cloud Solutions Office (ECSO). “When Databricks received FedRAMP High approval, we were able to move quickly to inherit that same Azure ATO and approve Azure Databricks for production workloads. Timing couldn’t have been better, as we have been working with a number of VA customers implementing Databricks for critical programs.”

Numerous federal agencies are looking to build cloud data lakes and leverage Delta Lake for a complete and consistent view of all their data,” said Kevin Davis, VP, Public Sector at Databricks. “The power of data and AI are being used to dramatically enhance public services, lower costs and improve quality of life for citizens. Using Azure Databricks, government agencies have aggregated hundreds of data sources to improve citizen outreach, automated processing of hourly utility infrastructure IoT data for predictive maintenance, deployed machine learning models to predict patient needs, and built dashboards to predict transportation needs and optimize logistics. FedRAMP High authorization for Azure Databricks further enables federal agencies to analyze all of their data for improved decision making and more accurate predictions.”

“We are excited to add Azure Databricks to our portfolio of services approved for FedRAMP at the high impact level in Azure Government,” said Lily Kim, General Manager of Azure Government at Microsoft. “Azure Government provides the most trusted cloud for mission-critical government workloads. FedRAMP High approval for Azure Databricks enables government customers to build fast and reliable data lakes for innovative new use cases, such as risk management and predictive analytics.”

With this certification, customers can now use Azure Databricks to process the U.S. government’s most sensitive, unclassified data in cloud computing environments, including data that involves the protection of life and financial assets. From personalized healthcare and education to space exploration and energy research, Azure Databricks enables organizations to accelerate new innovation while minimizing risk when working with highly sensitive, private and public sector data.

With FedRAMP, organizations have a consistent way to evaluate the security of cloud solutions using NIST and FISMA defined standards. FedRAMP offers several levels of assurance and Azure Databricks has met all of the requirements for authorization for the highest degree of assurance. Azure Databricks joins many other Azure services with the FedRAMP High authorization, enabling public sector, enterprise and industry vertical customers to create and deploy cloud-based applications with confidence.

Stockholm, Sweden, Nov. 17, 2020 (GLOBE NEWSWIRE) — Logical Clocks, the data company behind the world’s first Enterprise Feature Store for Machine Learning, today announced full support for Microsoft Azure on its cloud managed data platform, Hopsworks. The announcement complements the existing support for Amazon Web Service (AWS), thus enterprises can now manage features for training and serving models at scale on Hopsworks while maintaining control of their data inside their organisation’s accounts on the most popular cloud platforms.

Hopsworks is the only currently available cloud-native Enterprise Feature Store and it also includes a Data Science platform for developing and operating machine learning models in production.

“As Hopsworks is a modular platform with integrations to third party platforms, organisations can combine our Hopsworks Feature Store with their existing data stores and data science platforms in their cloud of choice, AWS or Azure,” explains Steffen Grohsschmiedt, Head of Cloud at Logical Clocks.

Hopsworks offers a free version which includes all functionalities needed for feature engineering, feature management, and ML model training. Hopsworks offers unlimited storage based on Azure Blob Storage as well as integrations with Databricks, SageMaker, and Kubeflow.

“Our mission is to hide the complexity of developing and operating machine learning at scale, for every company. We provide data scientists with the tools and platform to create the best possible pipeline for any use case. We are committed to making our platform best of breed and modular enough to work in any ecosystem,” concludes Steffen.

Logical Clocks is the data company that builds Hopsworks, the world’s first Enterprise Feature Store for Machine Learning. With offices in Stockholm, London and Palo Alto, Logical Clocks has set out to make machine learning easy for every company, providing tools for data scientists and engineers to manage features and develop/deploy models in production.

Databricks Runtime 5.5 with Conda (Beta)

The second beta release of Databricks Runtime with Conda arrived as version 5.5, bringing upgraded packages alongside several usability improvements for data engineering and data science teams working in Python.

• YAML support: Databricks Library Utilities now accepts YAML files for customising Python environments, giving teams a more readable and portable way to define dependencies.
• Better environment isolation: Environments scoped to individual notebook sessions are more reliably isolated from one another, reducing dependency conflicts between concurrent users.
• Simplified installation: The installation process received UX improvements to reduce the number of steps required to get a Conda-backed environment running.
• Faster startup commands: Commands executed immediately after starting a cluster are reported to run significantly faster in this release.

Docker Launches Three-Tier Technology Partner Program

To formalise its ecosystem relationships and give customers clearer visibility into the depth of each vendor’s integration, Docker introduced a structured Technology Partner Program with three tiers:

• Verified: Partners that have engaged directly with Docker and publish their container images through a verified publisher account on Docker Hub.
• Professional: Partners whose products have passed the standard Docker Enterprise certification process.
• Premier: The highest tier, representing the deepest level of integration with Docker Enterprise. Premier partners must pass a category-specific technology review in addition to standard certification.

The tiered structure is intended to help enterprise customers quickly assess how tightly a third-party product integrates with the Docker platform before making procurement or architectural decisions.

AWS Chatbot Beta Brings Notifications and Commands to Slack and Chime

AWS introduced a public beta of AWS Chatbot, a service that bridges AWS operational events with collaboration tools. In its initial preview, AWS Chatbot supports two chat platforms:

• Slack
• Amazon Chime

Once configured, teams can receive automated notifications from a range of AWS services directly in their chat channels, including:

• Amazon CloudWatch (alarms and metrics)
• Amazon GuardDuty (security findings)
• AWS Health (service health events)
• AWS Budgets (cost threshold alerts)
• AWS Security Hub (aggregated security findings)
• AWS CloudFormation (stack events)

Beyond passive notifications, the beta also allows team members to execute AWS commands directly from a chat message, reducing the need to switch contexts to the AWS Console for common operational tasks.

Azure Pipelines Previews Pipeline Caching and Pipeline Artifacts

Microsoft made two performance-focused features available as public previews in Azure Pipelines, both aimed at reducing the time developers spend waiting for CI/CD stages to complete.

Pipeline Caching

Pipeline caching allows the results of expensive, repeatable operations—such as package restoration or compilation—to be stored and reused across pipeline runs and stages. When the inputs to a cacheable step have not changed, the cached output is restored instead of re-executing the step, cutting down build times considerably for large dependency trees.

Pipeline Artifacts

Pipeline artifacts improve the speed of transferring build outputs between jobs and stages within a pipeline. The feature uses a deduplication strategy so that only net-new content is uploaded to the artifact store on each run, reducing both upload time and storage consumption compared to uploading full artifact sets on every execution.

GitLab 12.1.1 Bugfix Release

Shortly after the GitLab 12.1 release, users running self-managed instances reported a false-positive issue: valid CI pipelines were triggering incorrect quota violation notifications after upgrading. GitLab responded quickly with version 12.1.1, a targeted patch release that resolves the erroneous alerts.

GitLab stated that upgrading from 12.1 to 12.1.1 is straightforward and does not require any downtime, making it a low-risk update for teams affected by the spurious notifications.

Summary

The Data Engineering Bottleneck Slowing AI Adoption

Data integration and big data management solutions provider Attunity announced an analytics platform designed to automate streaming data pipelines for AI, machine learning, and data science initiatives. The core problem it addresses is a familiar one: data engineering teams cannot keep pace with the demand for real-time datasets that machine learning applications require.

Data integration and migration are often manual, complex, and time-consuming processes. By the time a dataset is assembled and delivered to data scientists, it is frequently outdated — undermining the value of the analysis before it even begins. Attunity’s approach targets this lag directly by combining efficient change data capture (CDC) at scale with automated data transformations running on Apache Spark.

Attunity for Databricks Unified Analytics Platform

The integration — Attunity for Databricks Unified Analytics Platform — provides continuous change data capture, delivery, and automated data refinement to produce analytics-ready datasets inside Databricks. Databricks’ Unified Analytics Platform unifies data processing and machine learning in a single environment, with Apache Spark as the high-performance processing engine underneath.

By pairing Attunity’s CDC pipeline automation with Databricks, organizations gain a path from raw transactional source data to clean, ML-ready datasets without writing custom integration code. This matters in practice because manual pipeline development is a significant time sink for data engineers — time that could otherwise be spent on higher-value work.

“Our ability to reach into virtually all enterprise systems and deliver real-time, analytics-ready and transactionally consistent data without coding is a powerful enabler for data scientists, allowing them to generate the business insights that accelerate corporate decision making and growth.”

Itamar Ankorion, Chief Marketing Officer, Attunity

Key Capabilities

Accelerated Time to Value

Using Apache Spark as the processing engine, Attunity automates data delivery and creates analytics-ready datasets in Databricks. Data engineers can build reusable, automated pipelines that reduce reliance on manual coding and expensive custom development resources, shortening the delivery cycle from source systems to data scientists.

Reduced Data Preparation Overhead

Attunity’s pipeline automation reduces the data preparation burden on data scientists. Rather than spending significant time wrangling raw data, data scientists can focus on analysis and model development. More agile pipeline generation means that changes to source data schemas or business requirements can be accommodated without rebuilding pipelines from scratch.

Real-Time Data with CDC at Scale

Attunity’s change data capture (CDC) technology provides the low-latency, high-throughput characteristics needed for large-scale machine learning datasets in the cloud. CDC captures only the changes made to source data — inserts, updates, and deletes — and streams those changes continuously, rather than running expensive bulk extractions on a schedule.

Multi-Cloud Support

Attunity supports Databricks deployments on both Microsoft Azure and Amazon Web Services (AWS), as well as a broad range of data lake, data warehouse, and streaming services available on those platforms. This gives organizations flexibility to adopt the platform within their existing cloud strategy without vendor lock-in to a single cloud provider.

Transactionally Consistent, ACID-Compliant Data

Attunity provides full support for the Databricks Unified Analytics Platform’s ACID capabilities. Transactions are delivered and applied in the order they were committed on the source system, ensuring that data scientists and ML models work from a transactionally consistent view of the data — a critical requirement for models where data ordering and integrity affect accuracy.

Why This Matters for DataOps

The Attunity-Databricks integration reflects a broader shift toward DataOps practices — applying DevOps-style automation, collaboration, and continuous delivery principles to data pipelines. Automating the movement and transformation of data reduces the manual coordination between data engineering and data science teams, shortens feedback loops, and makes it easier to keep ML models trained on current data.

For enterprises with data spread across siloed storage systems — on-premises databases, cloud data warehouses, streaming platforms — the ability to unify those sources into a single analytics environment without hand-coded ETL is a meaningful operational improvement. The combination of CDC for real-time ingestion and Spark for transformation automation addresses the two most time-consuming stages of the modern data pipeline.

Note: Attunity was acquired by Qlik in May 2019, shortly after this announcement. Organizations evaluating this technology should review current product branding and availability under Qlik.